This work analyses an algorithmic trading strategy based on cointegrated pairs of assets. The centrepiece of such a strategy is the discovery of tradable linear combinations consisting of two or more financial assets. An intuitive attempt to identify such linear asset combinations is based on statistical tests. An unrestricted testing of all possible linear combinations leads, however, to a multiple testing problem. Carrying out a suﬃciently large number of tests always gives significant results, even if not a single combination is truly cointegrated. This is in the nature of statistical tests. Well established correction methods like the popular Bonferroni correction turn out to be too conservative in such cases and make it impossible to discover even truly cointegrated combinations that would be highly profitable if applied in the proposed investment strategy.
A possible way to mitigate this problem can lie in the eﬀective pre-partitioning of the considered asset universe with the purpose of reducing the number of feasible combinations and, therefore, the number of statistical tests, to combinations with an increased potential of being profitable. This is the main contribution of this dissertation. Besides analysing the robustness of established cointegration tests with respect to particular strategy-relevant features, the main focus lies on possible ways to pre-partition the overall set of admissible assets. A carefully carried out back-testing finally inspects the eﬀectiveness of the proposed methods.
The back-testing results, which are based on an asset universe consisting of S&P 500 stocks and a time period stretching from January 1995 up to December 2011, show a very favourable picture. Apart from an attractive rate of return and a significantly smaller volatility as compared to the S&P 500 index, the returns of the applied pairs-trading strategies showed only a marginal correlation with the overall market returns.